{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-title"
    ]
   },
   "source": [
    "# Image features exercise\n",
    "*Complete and hand in this completed worksheet (including its outputs and any supporting code outside of the worksheet) with your assignment submission. For more details see the [assignments page](http://vision.stanford.edu/teaching/cs231n/assignments.html) on the course website.*\n",
    "\n",
    "We have seen that we can achieve reasonable performance on an image classification task by training a linear classifier on the pixels of the input image. In this exercise we will show that we can improve our classification performance by training linear classifiers not on raw pixels but on features that are computed from the raw pixels.\n",
    "\n",
    "All of your work for this exercise will be done in this notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [],
   "source": [
    "import random\n",
    "import numpy as np\n",
    "from cs231n.data_utils import load_CIFAR10\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "\n",
    "# for auto-reloading extenrnal modules\n",
    "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "source": [
    "## Load data\n",
    "Similar to previous exercises, we will load CIFAR-10 data from disk."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [],
   "source": [
    "from cs231n.features import color_histogram_hsv, hog_feature\n",
    "\n",
    "def get_CIFAR10_data(num_training=49000, num_validation=1000, num_test=1000):\n",
    "    # Load the raw CIFAR-10 data\n",
    "    cifar10_dir = 'cs231n/datasets/cifar-10-batches-py'\n",
    "\n",
    "    # Cleaning up variables to prevent loading data multiple times (which may cause memory issue)\n",
    "    try:\n",
    "       del X_train, y_train\n",
    "       del X_test, y_test\n",
    "       print('Clear previously loaded data.')\n",
    "    except:\n",
    "       pass\n",
    "\n",
    "    X_train, y_train, X_test, y_test = load_CIFAR10(cifar10_dir)\n",
    "    \n",
    "    # Subsample the data\n",
    "    mask = list(range(num_training, num_training + num_validation))\n",
    "    X_val = X_train[mask]\n",
    "    y_val = y_train[mask]\n",
    "    mask = list(range(num_training))\n",
    "    X_train = X_train[mask]\n",
    "    y_train = y_train[mask]\n",
    "    mask = list(range(num_test))\n",
    "    X_test = X_test[mask]\n",
    "    y_test = y_test[mask]\n",
    "    \n",
    "    return X_train, y_train, X_val, y_val, X_test, y_test\n",
    "\n",
    "X_train, y_train, X_val, y_val, X_test, y_test = get_CIFAR10_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train data shape:  (49000, 32, 32, 3)\n",
      "Train labels shape:  (49000,)\n",
      "Validation data shape:  (1000, 32, 32, 3)\n",
      "Validation labels shape:  (1000,)\n",
      "Test data shape:  (1000, 32, 32, 3)\n",
      "Test labels shape:  (1000,)\n"
     ]
    }
   ],
   "source": [
    "print('Train data shape: ', X_train.shape)\n",
    "print('Train labels shape: ', y_train.shape)\n",
    "print('Validation data shape: ', X_val.shape)\n",
    "print('Validation labels shape: ', y_val.shape)\n",
    "print('Test data shape: ', X_test.shape)\n",
    "print('Test labels shape: ', y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "source": [
    "## Extract Features\n",
    "For each image we will compute a Histogram of Oriented\n",
    "Gradients (HOG) as well as a color histogram using the hue channel in HSV\n",
    "color space. We form our final feature vector for each image by concatenating\n",
    "the HOG and color histogram feature vectors.\n",
    "\n",
    "Roughly speaking, HOG should capture the texture of the image while ignoring\n",
    "color information, and the color histogram represents the color of the input\n",
    "image while ignoring texture. As a result, we expect that using both together\n",
    "ought to work better than using either alone. Verifying this assumption would\n",
    "be a good thing to try for your own interest.\n",
    "\n",
    "The `hog_feature` and `color_histogram_hsv` functions both operate on a single\n",
    "image and return a feature vector for that image. The extract_features\n",
    "function takes a set of images and a list of feature functions and evaluates\n",
    "each feature function on each image, storing the results in a matrix where\n",
    "each column is the concatenation of all feature vectors for a single image."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true,
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done extracting features for 1000 / 49000 images\n",
      "Done extracting features for 2000 / 49000 images\n",
      "Done extracting features for 3000 / 49000 images\n",
      "Done extracting features for 4000 / 49000 images\n",
      "Done extracting features for 5000 / 49000 images\n",
      "Done extracting features for 6000 / 49000 images\n",
      "Done extracting features for 7000 / 49000 images\n",
      "Done extracting features for 8000 / 49000 images\n",
      "Done extracting features for 9000 / 49000 images\n",
      "Done extracting features for 10000 / 49000 images\n",
      "Done extracting features for 11000 / 49000 images\n",
      "Done extracting features for 12000 / 49000 images\n",
      "Done extracting features for 13000 / 49000 images\n",
      "Done extracting features for 14000 / 49000 images\n",
      "Done extracting features for 15000 / 49000 images\n",
      "Done extracting features for 16000 / 49000 images\n",
      "Done extracting features for 17000 / 49000 images\n",
      "Done extracting features for 18000 / 49000 images\n",
      "Done extracting features for 19000 / 49000 images\n",
      "Done extracting features for 20000 / 49000 images\n",
      "Done extracting features for 21000 / 49000 images\n",
      "Done extracting features for 22000 / 49000 images\n",
      "Done extracting features for 23000 / 49000 images\n",
      "Done extracting features for 24000 / 49000 images\n",
      "Done extracting features for 25000 / 49000 images\n",
      "Done extracting features for 26000 / 49000 images\n",
      "Done extracting features for 27000 / 49000 images\n",
      "Done extracting features for 28000 / 49000 images\n",
      "Done extracting features for 29000 / 49000 images\n",
      "Done extracting features for 30000 / 49000 images\n",
      "Done extracting features for 31000 / 49000 images\n",
      "Done extracting features for 32000 / 49000 images\n",
      "Done extracting features for 33000 / 49000 images\n",
      "Done extracting features for 34000 / 49000 images\n",
      "Done extracting features for 35000 / 49000 images\n",
      "Done extracting features for 36000 / 49000 images\n",
      "Done extracting features for 37000 / 49000 images\n",
      "Done extracting features for 38000 / 49000 images\n",
      "Done extracting features for 39000 / 49000 images\n",
      "Done extracting features for 40000 / 49000 images\n",
      "Done extracting features for 41000 / 49000 images\n",
      "Done extracting features for 42000 / 49000 images\n",
      "Done extracting features for 43000 / 49000 images\n",
      "Done extracting features for 44000 / 49000 images\n",
      "Done extracting features for 45000 / 49000 images\n",
      "Done extracting features for 46000 / 49000 images\n",
      "Done extracting features for 47000 / 49000 images\n",
      "Done extracting features for 48000 / 49000 images\n",
      "Done extracting features for 49000 / 49000 images\n"
     ]
    }
   ],
   "source": [
    "from cs231n.features import *\n",
    "\n",
    "num_color_bins = 10 # Number of bins in the color histogram\n",
    "feature_fns = [hog_feature, lambda img: color_histogram_hsv(img, nbin=num_color_bins)]\n",
    "X_train_feats = extract_features(X_train, feature_fns, verbose=True)\n",
    "X_val_feats = extract_features(X_val, feature_fns)\n",
    "X_test_feats = extract_features(X_test, feature_fns)\n",
    "\n",
    "# Preprocessing: Subtract the mean feature\n",
    "mean_feat = np.mean(X_train_feats, axis=0, keepdims=True)\n",
    "X_train_feats -= mean_feat\n",
    "X_val_feats -= mean_feat\n",
    "X_test_feats -= mean_feat\n",
    "\n",
    "# Preprocessing: Divide by standard deviation. This ensures that each feature\n",
    "# has roughly the same scale.\n",
    "std_feat = np.std(X_train_feats, axis=0, keepdims=True)\n",
    "X_train_feats /= std_feat\n",
    "X_val_feats /= std_feat\n",
    "X_test_feats /= std_feat\n",
    "\n",
    "# Preprocessing: Add a bias dimension\n",
    "X_train_feats = np.hstack([X_train_feats, np.ones((X_train_feats.shape[0], 1))])\n",
    "X_val_feats = np.hstack([X_val_feats, np.ones((X_val_feats.shape[0], 1))])\n",
    "X_test_feats = np.hstack([X_test_feats, np.ones((X_test_feats.shape[0], 1))])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train SVM on features\n",
    "Using the multiclass SVM code developed earlier in the assignment, train SVMs on top of the features extracted above; this should achieve better results than training SVMs directly on top of raw pixels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "tags": [
     "code"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iteration 0 / 1000: loss 89.053147\n",
      "iteration 100 / 1000: loss 87.466533\n",
      "iteration 200 / 1000: loss 85.919819\n",
      "iteration 300 / 1000: loss 84.405264\n",
      "iteration 400 / 1000: loss 82.904476\n",
      "iteration 500 / 1000: loss 81.449852\n",
      "iteration 600 / 1000: loss 80.010151\n",
      "iteration 700 / 1000: loss 78.600337\n",
      "iteration 800 / 1000: loss 77.200066\n",
      "iteration 900 / 1000: loss 75.867341\n",
      "iteration 0 / 1000: loss 780.833074\n",
      "iteration 100 / 1000: loss 640.864643\n",
      "iteration 200 / 1000: loss 526.270146\n",
      "iteration 300 / 1000: loss 432.463927\n",
      "iteration 400 / 1000: loss 355.670437\n",
      "iteration 500 / 1000: loss 292.796425\n",
      "iteration 600 / 1000: loss 241.337603\n",
      "iteration 700 / 1000: loss 199.202591\n",
      "iteration 800 / 1000: loss 164.703740\n",
      "iteration 900 / 1000: loss 136.472376\n",
      "iteration 0 / 1000: loss 7979.202370\n",
      "iteration 100 / 1000: loss 1076.846704\n",
      "iteration 200 / 1000: loss 152.068085\n",
      "iteration 300 / 1000: loss 28.168191\n",
      "iteration 400 / 1000: loss 11.568162\n",
      "iteration 500 / 1000: loss 9.344071\n",
      "iteration 600 / 1000: loss 9.046088\n",
      "iteration 700 / 1000: loss 9.006169\n",
      "iteration 800 / 1000: loss 9.000820\n",
      "iteration 900 / 1000: loss 9.000108\n",
      "iteration 0 / 1000: loss 85.683640\n",
      "iteration 100 / 1000: loss 71.764520\n",
      "iteration 200 / 1000: loss 60.381629\n",
      "iteration 300 / 1000: loss 51.070383\n",
      "iteration 400 / 1000: loss 43.427874\n",
      "iteration 500 / 1000: loss 37.190348\n",
      "iteration 600 / 1000: loss 32.080251\n",
      "iteration 700 / 1000: loss 27.888636\n",
      "iteration 800 / 1000: loss 24.467500\n",
      "iteration 900 / 1000: loss 21.658857\n",
      "iteration 0 / 1000: loss 784.748046\n",
      "iteration 100 / 1000: loss 112.931721\n",
      "iteration 200 / 1000: loss 22.924476\n",
      "iteration 300 / 1000: loss 10.865238\n",
      "iteration 400 / 1000: loss 9.249765\n",
      "iteration 500 / 1000: loss 9.033461\n",
      "iteration 600 / 1000: loss 9.004431\n",
      "iteration 700 / 1000: loss 9.000566\n",
      "iteration 800 / 1000: loss 9.000046\n",
      "iteration 900 / 1000: loss 8.999979\n",
      "iteration 0 / 1000: loss 7754.732859\n",
      "iteration 100 / 1000: loss 9.000002\n",
      "iteration 200 / 1000: loss 8.999997\n",
      "iteration 300 / 1000: loss 8.999997\n",
      "iteration 400 / 1000: loss 8.999996\n",
      "iteration 500 / 1000: loss 8.999997\n",
      "iteration 600 / 1000: loss 8.999996\n",
      "iteration 700 / 1000: loss 8.999996\n",
      "iteration 800 / 1000: loss 8.999997\n",
      "iteration 900 / 1000: loss 8.999997\n",
      "iteration 0 / 1000: loss 88.591276\n",
      "iteration 100 / 1000: loss 19.664730\n",
      "iteration 200 / 1000: loss 10.427883\n",
      "iteration 300 / 1000: loss 9.191465\n",
      "iteration 400 / 1000: loss 9.025435\n",
      "iteration 500 / 1000: loss 9.003028\n",
      "iteration 600 / 1000: loss 9.000158\n",
      "iteration 700 / 1000: loss 8.999725\n",
      "iteration 800 / 1000: loss 8.999686\n",
      "iteration 900 / 1000: loss 8.999650\n",
      "iteration 0 / 1000: loss 818.002724\n",
      "iteration 100 / 1000: loss 8.999963\n",
      "iteration 200 / 1000: loss 8.999976\n",
      "iteration 300 / 1000: loss 8.999966\n",
      "iteration 400 / 1000: loss 8.999972\n",
      "iteration 500 / 1000: loss 8.999973\n",
      "iteration 600 / 1000: loss 8.999969\n",
      "iteration 700 / 1000: loss 8.999959\n",
      "iteration 800 / 1000: loss 8.999970\n",
      "iteration 900 / 1000: loss 8.999965\n",
      "iteration 0 / 1000: loss 7867.287979\n",
      "iteration 100 / 1000: loss 8.999998\n",
      "iteration 200 / 1000: loss 9.000000\n",
      "iteration 300 / 1000: loss 9.000000\n",
      "iteration 400 / 1000: loss 9.000001\n",
      "iteration 500 / 1000: loss 9.000000\n",
      "iteration 600 / 1000: loss 8.999999\n",
      "iteration 700 / 1000: loss 8.999999\n",
      "iteration 800 / 1000: loss 9.000000\n",
      "iteration 900 / 1000: loss 9.000000\n",
      "lr 1.000000e-09 reg 5.000000e+04 train accuracy: 0.124163 val accuracy: 0.113000\n",
      "lr 1.000000e-09 reg 5.000000e+05 train accuracy: 0.093816 val accuracy: 0.094000\n",
      "lr 1.000000e-09 reg 5.000000e+06 train accuracy: 0.301735 val accuracy: 0.300000\n",
      "lr 1.000000e-08 reg 5.000000e+04 train accuracy: 0.090184 val accuracy: 0.086000\n",
      "lr 1.000000e-08 reg 5.000000e+05 train accuracy: 0.408041 val accuracy: 0.406000\n",
      "lr 1.000000e-08 reg 5.000000e+06 train accuracy: 0.401837 val accuracy: 0.381000\n",
      "lr 1.000000e-07 reg 5.000000e+04 train accuracy: 0.413000 val accuracy: 0.404000\n",
      "lr 1.000000e-07 reg 5.000000e+05 train accuracy: 0.417265 val accuracy: 0.425000\n",
      "lr 1.000000e-07 reg 5.000000e+06 train accuracy: 0.333327 val accuracy: 0.331000\n",
      "best validation accuracy achieved during cross-validation: 0.425000\n"
     ]
    }
   ],
   "source": [
    "# Use the validation set to tune the learning rate and regularization strength\n",
    "\n",
    "from cs231n.classifiers.linear_classifier import LinearSVM\n",
    "\n",
    "learning_rates = [1e-9, 1e-8, 1e-7]\n",
    "regularization_strengths = [5e4, 5e5, 5e6]\n",
    "\n",
    "results = {}\n",
    "best_val = -1\n",
    "best_svm = None\n",
    "\n",
    "################################################################################\n",
    "# TODO:                                                                        #\n",
    "# Use the validation set to set the learning rate and regularization strength. #\n",
    "# This should be identical to the validation that you did for the SVM; save    #\n",
    "# the best trained classifer in best_svm. You might also want to play          #\n",
    "# with different numbers of bins in the color histogram. If you are careful    #\n",
    "# you should be able to get accuracy of near 0.44 on the validation set.       #\n",
    "################################################################################\n",
    "# *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n",
    "\n",
    "for lr in learning_rates:\n",
    "    for rs in regularization_strengths:\n",
    "        mySvm = LinearSVM()\n",
    "        loss_hist = mySvm.train(X_train_feats, y_train, learning_rate=lr, reg=rs,\n",
    "                      num_iters=1000, verbose=True)\n",
    "        y_train_pred = mySvm.predict(X_train_feats)\n",
    "        train_acc = np.mean(y_train == y_train_pred)\n",
    "        y_val_pred = mySvm.predict(X_val_feats)\n",
    "        val_acc = np.mean(y_val == y_val_pred)\n",
    "        \n",
    "        results[(lr, rs)] = (train_acc, val_acc)\n",
    "        \n",
    "        if val_acc > best_val:\n",
    "            best_val = val_acc\n",
    "            best_svm = mySvm\n",
    "\n",
    "# *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n",
    "\n",
    "# Print out results.\n",
    "for lr, reg in sorted(results):\n",
    "    train_accuracy, val_accuracy = results[(lr, reg)]\n",
    "    print('lr %e reg %e train accuracy: %f val accuracy: %f' % (\n",
    "                lr, reg, train_accuracy, val_accuracy))\n",
    "    \n",
    "print('best validation accuracy achieved during cross-validation: %f' % best_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.413\n"
     ]
    }
   ],
   "source": [
    "# Evaluate your trained SVM on the test set\n",
    "y_test_pred = best_svm.predict(X_test_feats)\n",
    "test_accuracy = np.mean(y_test == y_test_pred)\n",
    "print(test_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 80 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# An important way to gain intuition about how an algorithm works is to\n",
    "# visualize the mistakes that it makes. In this visualization, we show examples\n",
    "# of images that are misclassified by our current system. The first column\n",
    "# shows images that our system labeled as \"plane\" but whose true label is\n",
    "# something other than \"plane\".\n",
    "\n",
    "examples_per_class = 8\n",
    "classes = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n",
    "for cls, cls_name in enumerate(classes):\n",
    "    idxs = np.where((y_test != cls) & (y_test_pred == cls))[0]\n",
    "    idxs = np.random.choice(idxs, examples_per_class, replace=False)\n",
    "    for i, idx in enumerate(idxs):\n",
    "        plt.subplot(examples_per_class, len(classes), i * len(classes) + cls + 1)\n",
    "        plt.imshow(X_test[idx].astype('uint8'))\n",
    "        plt.axis('off')\n",
    "        if i == 0:\n",
    "            plt.title(cls_name)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-inline"
    ]
   },
   "source": [
    "### Inline question 1:\n",
    "Describe the misclassification results that you see. Do they make sense?\n",
    "\n",
    "\n",
    "$\\color{blue}{\\textit Your Answer:}$\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Neural Network on image features\n",
    "Earlier in this assigment we saw that training a two-layer neural network on raw pixels achieved better classification performance than linear classifiers on raw pixels. In this notebook we have seen that linear classifiers on image features outperform linear classifiers on raw pixels. \n",
    "\n",
    "For completeness, we should also try training a neural network on image features. This approach should outperform all previous approaches: you should easily be able to achieve over 55% classification accuracy on the test set; our best model achieves about 60% classification accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(49000, 155)\n",
      "(49000, 154)\n"
     ]
    }
   ],
   "source": [
    "# Preprocessing: Remove the bias dimension\n",
    "# Make sure to run this cell only ONCE\n",
    "print(X_train_feats.shape)\n",
    "X_train_feats = X_train_feats[:, :-1]\n",
    "X_val_feats = X_val_feats[:, :-1]\n",
    "X_test_feats = X_test_feats[:, :-1]\n",
    "\n",
    "print(X_train_feats.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "tags": [
     "code"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lr = 0.100000, bs = 256, rs = 0.000100\n",
      "iteration 0 / 1500: loss 2.302585\n",
      "iteration 500 / 1500: loss 1.556969\n",
      "iteration 1000 / 1500: loss 1.351982\n",
      "val_acc = 0.532000\n",
      "==============================================================\n",
      "lr = 0.100000, bs = 256, rs = 0.000300\n",
      "iteration 0 / 1500: loss 2.302585\n",
      "iteration 500 / 1500: loss 1.529274\n",
      "iteration 1000 / 1500: loss 1.428434\n",
      "val_acc = 0.528000\n",
      "==============================================================\n",
      "lr = 0.100000, bs = 256, rs = 0.001000\n",
      "iteration 0 / 1500: loss 2.302586\n",
      "iteration 500 / 1500: loss 1.537822\n",
      "iteration 1000 / 1500: loss 1.262656\n",
      "val_acc = 0.523000\n",
      "==============================================================\n",
      "lr = 0.100000, bs = 256, rs = 0.003000\n",
      "iteration 0 / 1500: loss 2.302587\n",
      "iteration 500 / 1500: loss 1.559741\n",
      "iteration 1000 / 1500: loss 1.377117\n",
      "val_acc = 0.527000\n",
      "==============================================================\n",
      "lr = 0.100000, bs = 256, rs = 0.010000\n",
      "iteration 0 / 1500: loss 2.302593\n",
      "iteration 500 / 1500: loss 1.729828\n",
      "iteration 1000 / 1500: loss 1.655457\n",
      "val_acc = 0.519000\n",
      "==============================================================\n",
      "lr = 0.100000, bs = 512, rs = 0.000100\n",
      "iteration 0 / 1500: loss 2.302585\n",
      "iteration 500 / 1500: loss 1.477543\n",
      "iteration 1000 / 1500: loss 1.260613\n",
      "val_acc = 0.537000\n",
      "==============================================================\n",
      "lr = 0.100000, bs = 512, rs = 0.000300\n",
      "iteration 0 / 1500: loss 2.302585\n",
      "iteration 500 / 1500: loss 1.522797\n",
      "iteration 1000 / 1500: loss 1.286283\n",
      "val_acc = 0.538000\n",
      "==============================================================\n",
      "lr = 0.100000, bs = 512, rs = 0.001000\n",
      "iteration 0 / 1500: loss 2.302586\n",
      "iteration 500 / 1500: loss 1.541629\n",
      "iteration 1000 / 1500: loss 1.378425\n",
      "val_acc = 0.524000\n",
      "==============================================================\n",
      "lr = 0.100000, bs = 512, rs = 0.003000\n",
      "iteration 0 / 1500: loss 2.302588\n",
      "iteration 500 / 1500: loss 1.508827\n",
      "iteration 1000 / 1500: loss 1.438091\n",
      "val_acc = 0.517000\n",
      "==============================================================\n",
      "lr = 0.100000, bs = 512, rs = 0.010000\n",
      "iteration 0 / 1500: loss 2.302593\n",
      "iteration 500 / 1500: loss 1.658261\n",
      "iteration 1000 / 1500: loss 1.574051\n",
      "val_acc = 0.511000\n",
      "==============================================================\n",
      "lr = 0.300000, bs = 256, rs = 0.000100\n",
      "iteration 0 / 1500: loss 2.302585\n",
      "iteration 500 / 1500: loss 1.204064\n",
      "iteration 1000 / 1500: loss 1.168800\n",
      "val_acc = 0.584000\n",
      "==============================================================\n",
      "lr = 0.300000, bs = 256, rs = 0.000300\n",
      "iteration 0 / 1500: loss 2.302586\n",
      "iteration 500 / 1500: loss 1.327194\n",
      "iteration 1000 / 1500: loss 1.138897\n",
      "val_acc = 0.569000\n",
      "==============================================================\n",
      "lr = 0.300000, bs = 256, rs = 0.001000\n",
      "iteration 0 / 1500: loss 2.302586\n",
      "iteration 500 / 1500: loss 1.302646\n",
      "iteration 1000 / 1500: loss 1.267893\n",
      "val_acc = 0.598000\n",
      "==============================================================\n",
      "lr = 0.300000, bs = 256, rs = 0.003000\n",
      "iteration 0 / 1500: loss 2.302587\n",
      "iteration 500 / 1500: loss 1.356734\n",
      "iteration 1000 / 1500: loss 1.349072\n",
      "val_acc = 0.540000\n",
      "==============================================================\n",
      "lr = 0.300000, bs = 256, rs = 0.010000\n",
      "iteration 0 / 1500: loss 2.302593\n",
      "iteration 500 / 1500: loss 1.595052\n",
      "iteration 1000 / 1500: loss 1.489141\n",
      "val_acc = 0.517000\n",
      "==============================================================\n",
      "lr = 0.300000, bs = 512, rs = 0.000100\n",
      "iteration 0 / 1500: loss 2.302585\n",
      "iteration 500 / 1500: loss 1.351988\n",
      "iteration 1000 / 1500: loss 1.048214\n",
      "val_acc = 0.590000\n",
      "==============================================================\n",
      "lr = 0.300000, bs = 512, rs = 0.000300\n",
      "iteration 0 / 1500: loss 2.302585\n",
      "iteration 500 / 1500: loss 1.232908\n",
      "iteration 1000 / 1500: loss 1.169185\n",
      "val_acc = 0.600000\n",
      "==============================================================\n",
      "lr = 0.300000, bs = 512, rs = 0.001000\n",
      "iteration 0 / 1500: loss 2.302586\n",
      "iteration 500 / 1500: loss 1.346275\n",
      "iteration 1000 / 1500: loss 1.210766\n",
      "val_acc = 0.587000\n",
      "==============================================================\n",
      "lr = 0.300000, bs = 512, rs = 0.003000\n",
      "iteration 0 / 1500: loss 2.302587\n",
      "iteration 500 / 1500: loss 1.398577\n",
      "iteration 1000 / 1500: loss 1.401140\n",
      "val_acc = 0.569000\n",
      "==============================================================\n",
      "lr = 0.300000, bs = 512, rs = 0.010000\n",
      "iteration 0 / 1500: loss 2.302593\n",
      "iteration 500 / 1500: loss 1.613616\n",
      "iteration 1000 / 1500: loss 1.578353\n",
      "val_acc = 0.517000\n",
      "==============================================================\n",
      "lr = 1.000000, bs = 256, rs = 0.000100\n",
      "iteration 0 / 1500: loss 2.302585\n",
      "iteration 500 / 1500: loss 1.242410\n",
      "iteration 1000 / 1500: loss 0.951783\n",
      "val_acc = 0.554000\n",
      "==============================================================\n",
      "lr = 1.000000, bs = 256, rs = 0.000300\n",
      "iteration 0 / 1500: loss 2.302585\n",
      "iteration 500 / 1500: loss 1.265314\n",
      "iteration 1000 / 1500: loss 1.030578\n",
      "val_acc = 0.573000\n",
      "==============================================================\n",
      "lr = 1.000000, bs = 256, rs = 0.001000\n",
      "iteration 0 / 1500: loss 2.302586\n",
      "iteration 500 / 1500: loss 1.178478\n",
      "iteration 1000 / 1500: loss 1.197765\n",
      "val_acc = 0.570000\n",
      "==============================================================\n",
      "lr = 1.000000, bs = 256, rs = 0.003000\n",
      "iteration 0 / 1500: loss 2.302588\n",
      "iteration 500 / 1500: loss 1.392424\n",
      "iteration 1000 / 1500: loss 1.561428\n",
      "val_acc = 0.554000\n",
      "==============================================================\n",
      "lr = 1.000000, bs = 256, rs = 0.010000\n",
      "iteration 0 / 1500: loss 2.302593\n",
      "iteration 500 / 1500: loss 1.625533\n",
      "iteration 1000 / 1500: loss 1.549055\n",
      "val_acc = 0.485000\n",
      "==============================================================\n",
      "lr = 1.000000, bs = 512, rs = 0.000100\n",
      "iteration 0 / 1500: loss 2.302585\n",
      "iteration 500 / 1500: loss 1.147202\n",
      "iteration 1000 / 1500: loss 0.800420\n",
      "val_acc = 0.546000\n",
      "==============================================================\n",
      "lr = 1.000000, bs = 512, rs = 0.000300\n",
      "iteration 0 / 1500: loss 2.302585\n",
      "iteration 500 / 1500: loss 1.128781\n",
      "iteration 1000 / 1500: loss 0.998265\n",
      "val_acc = 0.577000\n",
      "==============================================================\n",
      "lr = 1.000000, bs = 512, rs = 0.001000\n",
      "iteration 0 / 1500: loss 2.302586\n",
      "iteration 500 / 1500: loss 1.286333\n",
      "iteration 1000 / 1500: loss 1.121775\n",
      "val_acc = 0.589000\n",
      "==============================================================\n",
      "lr = 1.000000, bs = 512, rs = 0.003000\n",
      "iteration 0 / 1500: loss 2.302588\n",
      "iteration 500 / 1500: loss 1.316160\n",
      "iteration 1000 / 1500: loss 1.370188\n",
      "val_acc = 0.551000\n",
      "==============================================================\n",
      "lr = 1.000000, bs = 512, rs = 0.010000\n",
      "iteration 0 / 1500: loss 2.302593\n",
      "iteration 500 / 1500: loss 1.603354\n",
      "iteration 1000 / 1500: loss 1.574274\n",
      "val_acc = 0.493000\n",
      "==============================================================\n",
      "Training took 0 hours 9 minutes 1 seconds\n"
     ]
    }
   ],
   "source": [
    "from cs231n.classifiers.neural_net import TwoLayerNet\n",
    "\n",
    "input_dim = X_train_feats.shape[1]\n",
    "hidden_dim = 500\n",
    "num_classes = 10\n",
    "\n",
    "net = TwoLayerNet(input_dim, hidden_dim, num_classes)\n",
    "best_net = None\n",
    "best_val = -1\n",
    "\n",
    "################################################################################\n",
    "# TODO: Train a two-layer neural network on image features. You may want to    #\n",
    "# cross-validate various parameters as in previous sections. Store your best   #\n",
    "# model in the best_net variable.                                              #\n",
    "################################################################################\n",
    "# *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n",
    "\n",
    "# Here you may change printing show by 500 iters per time in neural_net.py\n",
    "# The hidden_dim is given\n",
    "import time\n",
    "tic = time.time()\n",
    "learning_rates = [1e-1, 3e-1, 1e0]\n",
    "batch_sizes = [256, 512]\n",
    "regularization_strengths = [1e-4, 3e-4, 1e-3, 3e-3, 1e-2]\n",
    "# I use num_iters = 2000 uniformly\n",
    "for lr in learning_rates:\n",
    "    for bs in batch_sizes:\n",
    "        for rs in regularization_strengths:\n",
    "            net = TwoLayerNet(input_dim, hidden_dim, num_classes)\n",
    "            # Train the network\n",
    "            print('lr = %f, bs = %d, rs = %f' % (lr, bs, rs))\n",
    "            status = net.train(X_train_feats, y_train, X_val_feats, y_val,\n",
    "                                learning_rate=lr, reg=rs,\n",
    "                                num_iters=1500, batch_size=bs,\n",
    "                                learning_rate_decay=0.95, verbose=True)\n",
    "            val_accuracy = (net.predict(X_val_feats) == y_val).mean()\n",
    "            print('val_acc = %f' % (val_accuracy))\n",
    "            print('==============================================================')\n",
    "            if val_accuracy > best_val:\n",
    "                best_val = val_accuracy\n",
    "                best_net = net\n",
    "                \n",
    "toc = time.time()\n",
    "total_seconds = int(toc - tic)\n",
    "hours = total_seconds // 3600\n",
    "minutes = (total_seconds - hours * 3600) // 60\n",
    "seconds = total_seconds - hours * 3600 - minutes * 60\n",
    "print('Training took %d hours %d minutes %d seconds' % (hours, minutes, seconds))\n",
    "# *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The best validation accuracy is: 0.6\n"
     ]
    }
   ],
   "source": [
    "print('The best validation accuracy is:',best_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.572\n"
     ]
    }
   ],
   "source": [
    "# Run your best neural net classifier on the test set. You should be able\n",
    "# to get more than 55% accuracy.\n",
    "\n",
    "test_acc = (best_net.predict(X_test_feats) == y_test).mean()\n",
    "print(test_acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
